Overview

The project “IPL Win prediction” comes under the domain of Sports Analysis. In this project, we try to predict the winner of a cricket match based on data from cricket matches for the past ten years using Decision Tree algorithm and also evaluate if the algorithm is suited for this problem. Next, after cleaning and exploring the data, it is divided into two parts, training and testing, In the ratio of 70:30 so the algorithm can be trained and tested to further analyze it.

About the Data set

The data set used in this project is publicly available on Kaggle platform. The three data sets are “matches.csv” which has the data of every team which won and lost in the “Indian Premier League”(IPL) for the years 2008 - 2017. and “deliveries.csv” containing the play by play data of each match played. “IPl_Points_Table.csv” this data contains the points table data from the year 2008 - 2017.

The below table contains description of each variable present in the data.

Data preprocessing

The libraries used in this project include tidyverse, ggplot, and plotly, skimr, datadictonary. We used the “read.csv()” to import the datasets. Cleaning of the dataset we used the “filter()” function to remove the redundant rows and columns such as umpire3 column, rows with no result, changing the names of the team which describe the same team, and rows containing NA values.

# umpire3 columns only consists of NA values it is redundant information
matches$umpire3 <- NULL

# Removing the rows with no result
matches <- matches %>% filter(!(matches$result == c("no result", "tie")))

# Changing the name of a team because they are same
matches[matches == "Rising Pune Supergiant"] <- "Rising Pune Supergiants"

The IPL data set which we created is a combination of matches data as well as IPL_Points_Table because IPL_Points_Table has two variables Net.RR and Pts which are some key features used in the model Net.RR(Net Run rate) and Pts(Points in the season).

IplData <- matches %>% select(c(id,
                                team1,
                                team2,
                                toss_winner,
                                toss_decision,
                                winner))

IplData[IplData == "bat"] <- 1
IplData[IplData == "field"] <- 2

IplData <- left_join(IplData, PointsTable, by = c("team1" = "Team"))
IplData <- left_join(IplData, PointsTable, by = c("team2" = "Team"))
IplData$Pld.x <- NULL
IplData$Won.x <- NULL
IplData$Lost.x <- NULL
IplData$N.R.x <- NULL
IplData$Pld.y <- NULL
IplData$Won.y <- NULL
IplData$Lost.y <- NULL
IplData$N.R.y <- NULL

IplData <- IplData[complete.cases(IplData), ]

IplData <- IplData %>% rename(c(Net.RR_team1 = Net.RR.x,
                                Net.RR_team2 = Net.RR.y,
                                Pts.team1 = Pts.x,
                                Pts.team2 = Pts.y))
IplData$id <- NULL

IplData <- data.frame(IplData)

Split the Data into two parts

Before implementing the decision tree model we must split the data into two parts “train” data and “test” data in the ratio of 70:30 to train and test the model.

# Split the data into training and testing sets

library(caTools)
set.seed(123)
split = sample.split(IplData, SplitRatio = 0.70)
train = subset(IplData, split == TRUE)
test = subset(IplData, split == FALSE)

Data Analysis

The below plot shows the team with most number of wins.

MostToss <- matches %>% filter(toss_winner == winner) %>% count(toss_winner)

MostToss %>% ggplot(aes(x = fct_reorder(toss_winner, n), y = n, color = "black", fill = toss_winner)) + 
  geom_col() +
  coord_flip() +
  labs(title = "Most wins when a team won Toss",
       x = "Teams",
       y = "Number of wins",
       caption = "IPL Dataset of matches from 2008 - 2017") +
  theme_bw() +
  theme(legend.position = "")

This graph shows which team chose to bat first and win the most.

Mostwins_Batfirst <- matches %>% filter((toss_winner == winner) & (toss_decision == "bat")) %>% count(winner)

Mostwins_Batfirst %>% ggplot(aes(x = fct_reorder(winner, n), y = n, color = "black", fill = winner)) + 
  geom_col() +
  coord_flip() +
  labs(title = "Most wins by teams when choose to Bat first",
       x = "Teams",
       y = "Number of wins",
       caption = "IPL Dataset of matches from 2008 - 2017") +
  theme_bw() +
  theme(legend.position = "")

This below graph shows teams who chose to field first and win.

Mostwins_fieldfirst <- matches %>% filter((toss_winner == winner) & (toss_decision == "field")) %>% count(winner)

Mostwins_fieldfirst %>% ggplot(aes(x = fct_reorder(winner, n), y = n, color = "black", fill = winner)) + 
  geom_col() +
  coord_flip() +
  labs(title = "Most wins by teams when choose to Field first",
       x = "Teams",
       y = "Number of wins",
       caption = "IPL Dataset of matches from 2008 - 2017") +
  theme_bw() +
  theme(legend.position = "")

This below graph shows best players in the League.

Most_Player_of_match <- matches%>% count(player_of_match) %>% filter(n > 10)

Most_Player_of_match %>% ggplot(aes(x = fct_reorder(player_of_match, n), y = n, color = "black", fill = player_of_match)) +
  geom_col() +
  theme_bw() + 
  labs(title = " Players who won the most Player_of_Matches(MVP)",
       x = "Name of the Player",
       y = " Number of MVPs",
       caption =  "IPL Dataset of matches from 2008 - 2017") +
  theme(legend.position = " ",
        axis.text.x = element_text(angle = 45,
                                   size = 8,
                                   vjust = 0.70))

This below graph shows distribution of teams winning by more than 50 runs.

MostWin_byruns <- matches %>% filter(win_by_runs > 50) 

MostWin_byruns %>% ggplot(aes(x = win_by_runs, color = "black", fill = winner)) +
  geom_histogram() +
  labs(title = "Distribution of teams winning by more than 50 runs",
       x = "Win by Runs",
       y = "Number of times the team won",
       caption = "IPL Dataset of matches from 2008 - 2017") +
  theme_bw() +
  theme()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

This below graph shows the distribution of teams winning by more than 5 wickets.

MostWin_bywickets <- matches %>% filter(win_by_wickets >= 5)

MostWin_bywickets %>% ggplot(aes(x = win_by_wickets, color = "black", fill = winner)) +
  geom_histogram() +
  labs(title = "Distribution of teams winning by more than 5 wickets",
       x = "Win by wickets",
       y = "Number of times the team won",
       caption = "IPL Dataset of matches from 2008 - 2017") +
  theme_bw() +
  theme()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

This below plot shows most wins when field first

Match1 <- DeliveriesData %>% filter((match_id == 1))

Match1 %>% filter(inning == 1) %>% count(dismissal_kind)

##   dismissal_kind   n
## 1                121
## 2         bowled   1
## 3         caught   3

Match1 %>% filter(inning == 1) %>% count(batsman_runs)

##   batsman_runs  n
## 1            0 32
## 2            1 57
## 3            2  9
## 4            3  1
## 5            4 17
## 6            6  9

data <- Mostwins_fieldfirst

fig <- plot_ly(data, labels = ~winner, values = ~n, type = 'pie')
fig <- fig %>% layout(title = 'Match won when Field first',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

fig

Decision Tree Model

The basic concept behind decision trees is that each node in the tree represents a potential decision in the game. The branches of the tree are then used to identify the most likely outcome of the decision. For example, a decision tree might look at the relative strengths of a team’s offense(Net.RR) and decide whether it is more likely to win or lose the game. By analyzing the data and making a decision at every node, the decision tree can generate a prediction of the outcome of the game.

To implement the Decision Tree model we used the “rpart” and “rpart.plot” . and the train data which contains all the required features the main features used in this model are. toss_winner, toss_decision, Net.RR_team1, Net.RR_team2, Pts.team1, Pts.team2. These features are used to predict the winner of the game.

library(rpart)
library(rpart.plot)
library(rattle)

## Loading required package: bitops

## Rattle: A free graphical interface for data science with R.
## Version 5.5.1 Copyright (c) 2006-2021 Togaware Pty Ltd.
## Type 'rattle()' to shake, rattle, and roll your data.

## 
## Attaching package: 'rattle'

## The following object is masked from 'package:randomForest':
## 
##     importance

library(caret)

## Loading required package: lattice

## 
## Attaching package: 'caret'

## The following object is masked from 'package:purrr':
## 
##     lift

set.seed(222)

model1 <- rpart(winner ~ ., data = train, method = "class")

Evaluation of Model using Confusion Matrix

The confusion matrix is a table that is often used to describe the performance of a classification model on a set of test data for which the true values are known. It allows the user to see the model’s predictions in relation to the actual outcomes. We can use this information to calculate the accuracy of the model.

pred <- predict(model1, test, type = "class")

table_mat <- table(test$winner, pred)

accuracy_Test <- (sum(diag(table_mat)) / sum(table_mat))*100

accuracy_Test

## [1] 3.401361

print(paste('Accuracy for test', accuracy_Test))

## [1] "Accuracy for test 3.40136054421769"

From the confusion matrix we can understand that this model of Decision Tree is performing at an accuracy of 3.4% which is quite less than what we have expected. This may be due to under fitting of the model.

Future Scope

In future we can improve on this project by using the dimensionality reduction technique and using different models to train the data. Further, after generating an accurate model we can create a website or app where users can interact and predict the winner of a game.

links:

“https://www.researchgate.net/profile/Hamid-Rastegari-2/publication/ 262560138_A_Review_of_Data_Mining_Techniques_for_Result_Prediction_in_Sports/ links/00b7d537f853eba5fe000000/ A-Review-of-Data-Mining-Techniques-for-Result-Prediction-in-Sports.pdf”

“https://www.kaggle.com/code/akshitrai/ml-in-everything-ipl-winner-prediction”

“https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/ widm.1380?casa_token=2XItw_L8N2cAAAAA%3AQBNGTCiw7x36yKoE5din8w37QbWwc2Dkx7ldBeBu hfHMuMqlP2yZNrHEloAKCm7jpI6Uj8LDdtw7NDjT”